Collective unary decision-making by decentralized multiple-robot systems applied to the task-sequencing problem

Abstract

When a complex mission must be undertaken, it often can be simplified by dividing it into a sequence of smaller subtasks, which are then completed in order. This strategy implicitly requires a system to recognize the completion of each subtask and make the decision to begin work on the next one. Decentralized multiple-robot systems can tackle many tasks, but their behavior is typified by continuous responses to stimuli. Task sequencing, however, demands a controlled, self-induced phase change in collective behavior-working on one task one moment and then on a different task the next-which is nontrivial for an emergent system. The main contribution of this study is a collective decision-making framework for decentralized multiple-robot systems that enables such a system to cooperatively decide that a current task has been completed and thus focus its attention on the next one in a sequence using only anonymous local communication. Central to the framework is the use of consensus, whereby task sequencing is delayed until a prespecified proportion of a system's robots agree that the current task is complete, reducing the likelihood of premature decisions. Two low-cost consensus estimation strategies are presented, both of which are practical for the extremely simple robots that are expected to compose large decentralized systems. Experiments in simulation and with real robots demonstrate that the proposed decision-making framework performs as predicted. Although the specific application of collective decision-making in this work is the cooperative task-sequencing problem, the proposed decision-making framework potentially has many additional applications.

The Best-of-n Problem in Robot Swarms: Formalization, State of the Art, and Novel Perspectives

“…When the option quality is static, designers favor collective decision-making strategies that results in consensus decisions (Parker and Zhang, 2009;Montes de Oca et al, 2011;Scheidler et al, 2016). Differently, when the option quality is dynamic, i.e., a function of time, designers favor strategies that result in a large majority of robots in the swarm favoring the same option without converging to consensus (Parker and Zhang, 2010;Arvin et al, 2014). In this case, the remaining minority of agents that are not aligned with the current collective decision keep exploring other options and possibly discover new ones, making the swarm adaptive to changes in the environment (Schmickl et al, 2009b).…”

“…Later, Parker and Zhang (2011) considered a simplified version of this strategy and proposed a rate equation model to study its convergence properties. Parker and Zhang (2010) proposed a collective decisionmaking strategy for unary decisions and applied it to a tasksequencing problem (see Section 4.3). The authors proposed a quorum-sensing strategy to address this problem.…”

Collective decision with 100 Kilobots: speed versus accuracy in binary discrimination problems

“…In turns, this result means that the designer has a tool to fine-tune the desired proportion of agents exploring or disseminating at consensus. This could be of interest during a foraging task [24,41,36] to effectively tune the foraging rate, or to aid the calibration of the quorum thresholds [25,26] when the detection of consensus is necessary to trigger a change in the behavior of the entire swarm (e.g., migration to the selected site). The third equilibriumγ 3 in Equation (6) corresponds instead to a macroscopic state of indecision where both opinions coexist in the swarm.…”

Distributed Colony-Level Algorithm Switching for Robot Swarm Foraging

“…It is common for individual robots to switch between the behaviors of food collecting, obstacle avoidance, and resting, for example. Parker has studied distributed consensus in a swarm setting, using it to enable the swarm to move between subtasks in an overall task [17]. We focus on foraging, and do not use formal distributed consensus (or assume that our robots are`well-stirred ').…”